Design and Optimization of Low-Dropout Voltage Regulator Using Relational Graph Neural Network and Reinforcement Learning in Open-Source SKY130 Process
This is the source code for the paper accepted to IEEE/ACM ICCAAD 2023. The final accepted manuscript can be found here: https://www.zonghaoli.com/ml_analog_ic.html. The final accepted paper can be found here: https://ieeexplore.ieee.org/document/10323720.
All files you are looking for are placed inside the folder /python.
ldo.py: define the LDO1 environment (Gymnasium compatible).ldo_folded_cascode.py: define the LDO2 environment (Gymnasium compatible).ckt_graphs.py: define the graph info as well as specifications for LDO1 and LDO2.dev_params.py: used to extract device parameters such as threshold voltage and transconductance of transistors, providing the observations for RL.ddpg.py: where the DDPG algorithm is stored.models.py: where various GNN models are stored.main.py: run the optimization for LDO1.main2.py: run the optimization for LDO2.postproc.py: do some post-processing such as data visualization for LDO1 after the optimization is done.postproc.py: do some post-processing such as data visualization for LDO2 after the optimization is done.utils.py: some useful utilities./simulations: store the SPICE files and it is where Ngspice is running.ldo_rgcn_rl.ipynb: a notebook summaries the work, it is outdated.
You may just follow the tutorial in ldo_rgcn_rl.ipynb, which is outdated but the flow is the same.
If you want to have the update-to-date version running, simply execute main.py for LDO1 and main2.pyfor LDO2.